{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Image features exercise\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "We have seen that we can achieve reasonable performance on an image classification task by training a linear classifier on the pixels of the input image. In this exercise we will show that we can improve our classification performance by training linear classifiers not on raw pixels but on features that are computed from the raw pixels.\n",
    "\n",
    "All of your work for this exercise will be done in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading extenrnal modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## Load data\n",
    "Similar to previous exercises, we will load CIFAR-10 data from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "from cs231n.features import color_histogram_hsv, hog_feature\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "    # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "    try:\n",
    "       del X_train, y_train\n",
    "       del X_test, y_test\n",
    "       print('Clear previously loaded data.')\n",
    "    except:\n",
    "       pass\n",
    "\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "    \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "    \n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## Extract Features\n",
    "For each image we will compute a Histogram of Oriented\n",
    "Gradients (HOG) as well as a color histogram using the hue channel in HSV\n",
    "color space. We form our final feature vector for each image by concatenating\n",
    "the HOG and color histogram feature vectors.\n",
    "\n",
    "Roughly speaking, HOG should capture the texture of the image while ignoring\n",
    "color information, and the color histogram represents the color of the input\n",
    "image while ignoring texture. As a result, we expect that using both together\n",
    "ought to work better than using either alone. Verifying this assumption would\n",
    "be a good thing to try for your own interest.\n",
    "\n",
    "The `hog_feature` and `color_histogram_hsv` functions both operate on a single\n",
    "image and return a feature vector for that image. The extract_features\n",
    "function takes a set of images and a list of feature functions and evaluates\n",
    "each feature function on each image, storing the results in a matrix where\n",
    "each column is the concatenation of all feature vectors for a single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true,
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Done extracting features for 49000 / 49000 images\n"
     ]
    }
   ],
   "source": [
    "from cs231n.features import *\n",
    "\n",
    "num_color_bins = 25 # Number of bins in the color histogram\n",
    "feature_fns = [hog_feature, lambda img: color_histogram_hsv(img, nbin=num_color_bins)]\n",
    "X_train_feats = extract_features(X_train, feature_fns, verbose=True)\n",
    "X_val_feats = extract_features(X_val, feature_fns)\n",
    "X_test_feats = extract_features(X_test, feature_fns)\n",
    "\n",
    "# Preprocessing: Subtract the mean feature\n",
    "mean_feat = np.mean(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats -= mean_feat\n",
    "X_val_feats -= mean_feat\n",
    "X_test_feats -= mean_feat\n",
    "\n",
    "# Preprocessing: Divide by standard deviation. This ensures that each feature\n",
    "# has roughly the same scale.\n",
    "std_feat = np.std(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats /= std_feat\n",
    "X_val_feats /= std_feat\n",
    "X_test_feats /= std_feat\n",
    "\n",
    "# Preprocessing: Add a bias dimension\n",
    "X_train_feats = np.hstack([X_train_feats, np.ones((X_train_feats.shape[0], 1))])\n",
    "X_val_feats = np.hstack([X_val_feats, np.ones((X_val_feats.shape[0], 1))])\n",
    "X_test_feats = np.hstack([X_test_feats, np.ones((X_test_feats.shape[0], 1))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train SVM on features\n",
    "Using the multiclass SVM code developed earlier in the assignment, train SVMs on top of the features extracted above; this should achieve better results than training SVMs directly on top of raw pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-09 reg 5.000000e+04 train accuracy: 0.105490 val accuracy: 0.092000\n",
      "lr 1.000000e-09 reg 5.000000e+05 train accuracy: 0.115082 val accuracy: 0.107000\n",
      "lr 1.000000e-09 reg 5.000000e+06 train accuracy: 0.422653 val accuracy: 0.422000\n",
      "lr 1.000000e-08 reg 5.000000e+04 train accuracy: 0.084939 val accuracy: 0.087000\n",
      "lr 1.000000e-08 reg 5.000000e+05 train accuracy: 0.422755 val accuracy: 0.418000\n",
      "lr 1.000000e-08 reg 5.000000e+06 train accuracy: 0.417837 val accuracy: 0.416000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.424653 val accuracy: 0.424000\n",
      "lr 1.000000e-07 reg 5.000000e+05 train accuracy: 0.416653 val accuracy: 0.425000\n",
      "lr 1.000000e-07 reg 5.000000e+06 train accuracy: 0.365184 val accuracy: 0.363000\n",
      "best validation accuracy achieved during cross-validation: 0.425000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune the learning rate and regularization strength\n",
    "\n",
    "from cs231n.classifiers.linear_classifier import LinearSVM\n",
    "\n",
    "learning_rates = [1e-9, 1e-8, 1e-7]\n",
    "regularization_strengths = [5e4, 5e5, 5e6]\n",
    "\n",
    "results = {}\n",
    "best_val = -1\n",
    "best_svm = None\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Use the validation set to set the learning rate and regularization strength. #\n",
    "# This should be identical to the validation that you did for the SVM; save    #\n",
    "# the best trained classifer in best_svm. You might also want to play          #\n",
    "# with different numbers of bins in the color histogram. If you are careful    #\n",
    "# you should be able to get accuracy of near 0.44 on the validation set.       #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "params = [(x, y) for x in learning_rates for y in regularization_strengths]\n",
    "for lr, reg in params:\n",
    "    svm = LinearSVM()\n",
    "    loss_hist = svm.train(X_train_feats, y_train, learning_rate=lr, reg=reg, \n",
    "                          num_iters=1500, batch_size=400, verbose=False)\n",
    "    acc_train = (svm.predict(X_train_feats)==y_train).mean()\n",
    "    acc_val = (svm.predict(X_val_feats)==y_val).mean()\n",
    "    results[(lr, reg)] = (acc_train, acc_val)\n",
    "    if acc_val > best_val:\n",
    "        best_svm = svm\n",
    "        best_val = acc_val\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.409\n"
     ]
    }
   ],
   "source": [
    "# Evaluate your trained SVM on the test set\n",
    "y_test_pred = best_svm.predict(X_test_feats)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print(test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 80 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# An important way to gain intuition about how an algorithm works is to\n",
    "# visualize the mistakes that it makes. In this visualization, we show examples\n",
    "# of images that are misclassified by our current system. The first column\n",
    "# shows images that our system labeled as \"plane\" but whose true label is\n",
    "# something other than \"plane\".\n",
    "\n",
    "examples_per_class = 8\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for cls, cls_name in enumerate(classes):\n",
    "    idxs = np.where((y_test != cls) & (y_test_pred == cls))[0]\n",
    "    idxs = np.random.choice(idxs, examples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt.subplot(examples_per_class, len(classes), i * len(classes) + cls + 1)\n",
    "        plt.imshow(X_test[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls_name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "### Inline question 1:\n",
    "Describe the misclassification results that you see. Do they make sense?\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network on image features\n",
    "Earlier in this assigment we saw that training a two-layer neural network on raw pixels achieved better classification performance than linear classifiers on raw pixels. In this notebook we have seen that linear classifiers on image features outperform linear classifiers on raw pixels. \n",
    "\n",
    "For completeness, we should also try training a neural network on image features. This approach should outperform all previous approaches: you should easily be able to achieve over 55% classification accuracy on the test set; our best model achieves about 60% classification accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 170)\n",
      "(49000, 169)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: Remove the bias dimension\n",
    "# Make sure to run this cell only ONCE\n",
    "print(X_train_feats.shape)\n",
    "X_train_feats = X_train_feats[:, :-1]\n",
    "X_val_feats = X_val_feats[:, :-1]\n",
    "X_test_feats = X_test_feats[:, :-1]\n",
    "\n",
    "print(X_train_feats.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 2000: loss 2.302585\n",
      "iteration 100 / 2000: loss 2.301460\n",
      "iteration 200 / 2000: loss 2.140710\n",
      "iteration 300 / 2000: loss 1.802354\n",
      "iteration 400 / 2000: loss 1.578939\n",
      "iteration 500 / 2000: loss 1.486122\n",
      "iteration 600 / 2000: loss 1.406312\n",
      "iteration 700 / 2000: loss 1.500037\n",
      "iteration 800 / 2000: loss 1.407520\n",
      "iteration 900 / 2000: loss 1.318391\n",
      "iteration 1000 / 2000: loss 1.384671\n",
      "iteration 1100 / 2000: loss 1.434669\n",
      "iteration 1200 / 2000: loss 1.305952\n",
      "iteration 1300 / 2000: loss 1.338269\n",
      "iteration 1400 / 2000: loss 1.306817\n",
      "iteration 1500 / 2000: loss 1.208881\n",
      "iteration 1600 / 2000: loss 1.289420\n",
      "iteration 1700 / 2000: loss 1.281441\n",
      "iteration 1800 / 2000: loss 1.231705\n",
      "iteration 1900 / 2000: loss 1.310655\n",
      "lr 1.000000e-01 reg 3.000000e-04 train accuracy: 0.560776 val accuracy: 0.547000\n",
      "iteration 0 / 2000: loss 2.302585\n",
      "iteration 100 / 2000: loss 2.302519\n",
      "iteration 200 / 2000: loss 2.193877\n",
      "iteration 300 / 2000: loss 1.816055\n",
      "iteration 400 / 2000: loss 1.639640\n",
      "iteration 500 / 2000: loss 1.497296\n",
      "iteration 600 / 2000: loss 1.394405\n",
      "iteration 700 / 2000: loss 1.421047\n",
      "iteration 800 / 2000: loss 1.333228\n",
      "iteration 900 / 2000: loss 1.481332\n",
      "iteration 1000 / 2000: loss 1.330637\n",
      "iteration 1100 / 2000: loss 1.325359\n",
      "iteration 1200 / 2000: loss 1.361537\n",
      "iteration 1300 / 2000: loss 1.328125\n",
      "iteration 1400 / 2000: loss 1.349080\n",
      "iteration 1500 / 2000: loss 1.301244\n",
      "iteration 1600 / 2000: loss 1.307586\n",
      "iteration 1700 / 2000: loss 1.295870\n",
      "iteration 1800 / 2000: loss 1.289823\n",
      "iteration 1900 / 2000: loss 1.274976\n",
      "lr 1.000000e-01 reg 1.000000e-03 train accuracy: 0.559000 val accuracy: 0.539000\n",
      "iteration 0 / 2000: loss 2.302586\n",
      "iteration 100 / 2000: loss 2.302708\n",
      "iteration 200 / 2000: loss 2.184487\n",
      "iteration 300 / 2000: loss 1.837038\n",
      "iteration 400 / 2000: loss 1.679616\n",
      "iteration 500 / 2000: loss 1.529240\n",
      "iteration 600 / 2000: loss 1.483237\n",
      "iteration 700 / 2000: loss 1.369273\n",
      "iteration 800 / 2000: loss 1.413764\n",
      "iteration 900 / 2000: loss 1.473119\n",
      "iteration 1000 / 2000: loss 1.406658\n",
      "iteration 1100 / 2000: loss 1.385757\n",
      "iteration 1200 / 2000: loss 1.477226\n",
      "iteration 1300 / 2000: loss 1.344258\n",
      "iteration 1400 / 2000: loss 1.417260\n",
      "iteration 1500 / 2000: loss 1.295848\n",
      "iteration 1600 / 2000: loss 1.469855\n",
      "iteration 1700 / 2000: loss 1.389923\n",
      "iteration 1800 / 2000: loss 1.330952\n",
      "iteration 1900 / 2000: loss 1.388842\n",
      "lr 1.000000e-01 reg 3.000000e-03 train accuracy: 0.549122 val accuracy: 0.533000\n",
      "iteration 0 / 2000: loss 2.302585\n",
      "iteration 100 / 2000: loss 1.482561\n",
      "iteration 200 / 2000: loss 1.423299\n",
      "iteration 300 / 2000: loss 1.247402\n",
      "iteration 400 / 2000: loss 1.275276\n",
      "iteration 500 / 2000: loss 1.158545\n",
      "iteration 600 / 2000: loss 1.156772\n",
      "iteration 700 / 2000: loss 1.177075\n",
      "iteration 800 / 2000: loss 1.148726\n",
      "iteration 900 / 2000: loss 1.137346\n",
      "iteration 1000 / 2000: loss 1.015775\n",
      "iteration 1100 / 2000: loss 1.107392\n",
      "iteration 1200 / 2000: loss 1.053646\n",
      "iteration 1300 / 2000: loss 0.971209\n",
      "iteration 1400 / 2000: loss 0.978849\n",
      "iteration 1500 / 2000: loss 1.034752\n",
      "iteration 1600 / 2000: loss 0.889291\n",
      "iteration 1700 / 2000: loss 0.856627\n",
      "iteration 1800 / 2000: loss 0.884202\n",
      "iteration 1900 / 2000: loss 0.815739\n",
      "lr 5.000000e-01 reg 3.000000e-04 train accuracy: 0.719755 val accuracy: 0.603000\n",
      "iteration 0 / 2000: loss 2.302586\n",
      "iteration 100 / 2000: loss 1.552534\n",
      "iteration 200 / 2000: loss 1.315485\n",
      "iteration 300 / 2000: loss 1.461146\n",
      "iteration 400 / 2000: loss 1.299463\n",
      "iteration 500 / 2000: loss 1.282700\n",
      "iteration 600 / 2000: loss 1.244230\n",
      "iteration 700 / 2000: loss 1.086695\n",
      "iteration 800 / 2000: loss 1.150862\n",
      "iteration 900 / 2000: loss 1.175607\n",
      "iteration 1000 / 2000: loss 1.135430\n",
      "iteration 1100 / 2000: loss 1.124813\n",
      "iteration 1200 / 2000: loss 1.144378\n",
      "iteration 1300 / 2000: loss 1.127393\n",
      "iteration 1400 / 2000: loss 1.104509\n",
      "iteration 1500 / 2000: loss 1.108109\n",
      "iteration 1600 / 2000: loss 1.127128\n",
      "iteration 1700 / 2000: loss 1.139171\n",
      "iteration 1800 / 2000: loss 1.115414\n",
      "iteration 1900 / 2000: loss 1.073578\n",
      "lr 5.000000e-01 reg 1.000000e-03 train accuracy: 0.689143 val accuracy: 0.595000\n",
      "iteration 0 / 2000: loss 2.302586\n",
      "iteration 100 / 2000: loss 1.615712\n",
      "iteration 200 / 2000: loss 1.397666\n",
      "iteration 300 / 2000: loss 1.434997\n",
      "iteration 400 / 2000: loss 1.314775\n",
      "iteration 500 / 2000: loss 1.325101\n",
      "iteration 600 / 2000: loss 1.293057\n",
      "iteration 700 / 2000: loss 1.305898\n",
      "iteration 800 / 2000: loss 1.257831\n",
      "iteration 900 / 2000: loss 1.358684\n",
      "iteration 1000 / 2000: loss 1.276692\n",
      "iteration 1100 / 2000: loss 1.317219\n",
      "iteration 1200 / 2000: loss 1.250695\n",
      "iteration 1300 / 2000: loss 1.292299\n",
      "iteration 1400 / 2000: loss 1.374643\n",
      "iteration 1500 / 2000: loss 1.314334\n",
      "iteration 1600 / 2000: loss 1.320597\n",
      "iteration 1700 / 2000: loss 1.344115\n",
      "iteration 1800 / 2000: loss 1.370084\n",
      "iteration 1900 / 2000: loss 1.228186\n",
      "lr 5.000000e-01 reg 3.000000e-03 train accuracy: 0.621082 val accuracy: 0.586000\n",
      "best validation accuracy achieved during cross-validation: 0.603000\n"
     ]
    }
   ],
   "source": [
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "input_size = X_train_feats.shape[1]\n",
    "hidden_size = 200\n",
    "num_classes = 10\n",
    "\n",
    "#net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n",
    "best_net = None\n",
    "\n",
    "################################################################################\n",
    "# TODO: Train a two-layer neural network on image features. You may want to    #\n",
    "# cross-validate various parameters as in previous sections. Store your best   #\n",
    "# model in the best_net variable.                                              #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "#input_size = 32 * 32 * 3\n",
    "#hidden_size = 300\n",
    "num_classes = 10\n",
    "best_val = -1.0\n",
    "best_stats = None\n",
    "results = {}\n",
    "\n",
    "learning_rates = [1e-1, 5e-1]\n",
    "reg_strengths = [3e-4, 1e-3, 3e-3]\n",
    "\n",
    "params = [(x,y) for x in learning_rates for y in reg_strengths]\n",
    "for lr, reg in params:\n",
    "    net = TwoLayerNet(input_size, hidden_size, num_classes)\n",
    "    stats = net.train(X_train_feats, y_train, X_val_feats, y_val,\n",
    "            num_iters=2000, batch_size=400,\n",
    "            learning_rate=lr, learning_rate_decay=0.95,\n",
    "            reg=reg, verbose=True)\n",
    "    acc_train = (net.predict(X_train_feats)==y_train).mean()\n",
    "    acc_val = (net.predict(X_val_feats)==y_val).mean()\n",
    "    results[(lr, reg)] = (acc_train, acc_val)\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, acc_train, acc_val))    \n",
    "    if acc_val > best_val:\n",
    "        best_net = net\n",
    "        best_val = acc_val\n",
    "        best_stats = stats\n",
    "\n",
    "# print results\n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.589\n"
     ]
    }
   ],
   "source": [
    "# Run your best neural net classifier on the test set. You should be able\n",
    "# to get more than 55% accuracy.\n",
    "\n",
    "# Plot the loss function and train / validation accuracies\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(best_stats['loss_history'])\n",
    "plt.title('Loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(best_stats['train_acc_history'], label='train')\n",
    "plt.plot(best_stats['val_acc_history'], label='val')\n",
    "plt.title('Classification accuracy history')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Classification accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "test_acc = (best_net.predict(X_test_feats) == y_test).mean()\n",
    "print(test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
